From Military to ‘Security Interventions’: An Alternative Approach to Contemporary Interventions
Bibliographic record
Abstract
In both academic and policy circles international interventions tend to mean ‘military’ interventions and debates tend to focus on whether such interventions are ‘good’ or ‘bad’ in general. This article aims to open up scholarly engagement on the topic of the thorny reality of interventions in different contexts by reconceptualising international interventions as ‘security interventions.’ The article draws attention to the ambiguous meaning of ‘security’ as both an objective (i.e. safety) as well as a practice (military forces, police, intelligence agencies and their tactics), something that is reflected in the different approaches to be gleaned from the security studies literature. From this ambiguity, it derives two interlinked concepts: ‘security culture’ and ‘security gap,’ as analytical tools to grasp the complexity of international interventions. The concept of ‘security culture’ captures specific combinations of objectives and practices. The concept of ‘security gap’ captures the particular relationship or the distinct kind of ‘mismatch’ between objectives and practices as it occurs in a ‘security culture.’ This reading of international interventions through the concept of ‘security culture’ and the interlinked analytical tool ‘security gap’ allows an analysis and understanding that goes beyond simplistic assumptions both about traditional military capabilities and the role of the ‘international community’ as a unitary actor.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.006 | 0.065 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".